def test_eegRDM(self):
eeg_data = np.random.rand(8, 10, 15, 32, 50)
rdms = eegRDM(eeg_data, sub_opt=0, chl_opt=0, time_opt=0)
self.assertEqual(rdms.shape[0], 8)
rdms = eegRDM(eeg_data, sub_opt=0, chl_opt=0, time_opt=1)
self.assertEqual(rdms.shape[0], 10)
rdms = eegRDM(eeg_data, sub_opt=0, chl_opt=1, time_opt=0)
self.assertEqual(rdms.shape[0], 32)
rdms = eegRDM(eeg_data, sub_opt=0, chl_opt=1, time_opt=1)
self.assertEqual(rdms.shape[0], 32)
rdms = eegRDM(eeg_data, sub_opt=1, chl_opt=0, time_opt=0)
self.assertEqual(rdms.shape[0], 10)
rdms = eegRDM(eeg_data, sub_opt=1, chl_opt=0, time_opt=1)
self.assertEqual(rdms.shape[0], 10)
rdms = eegRDM(eeg_data, sub_opt=1, chl_opt=1, time_opt=0)
self.assertEqual(rdms.shape[0], 10)
rdms = eegRDM(eeg_data, sub_opt=1, chl_opt=1, time_opt=1)
self.assertEqual(rdms.shape[0], 10)
eeg_data = np.random.rand(8, 10, 15, 32)
output = bhvRDM(eeg_data)
self.assertEqual(output, "Invalid input!")
nps = nps(nps_data, time_win=10, time_step=10)
# Plot the NPS results
plot_nps_hotmap(nps[:, :, 0], time_unit=[0, 0.01])
# Smooth the results and plot
plot_nps_hotmap(nps[:, :, 0], time_unit=[0, 0.01], smooth=True)
# 加上轮廓
rsa_plot.plot_stats_hotmap(nps, time_unit=[0, 0.01], smooth=True, outline=True)
#%%
"""********** Section 4: Calculating single RDM and Plotting **********"""
# Calculate the RDM based on the data during 190ms-210ms
rdm = eegRDM(megdata[:, :, :, :, 290:310])
# Plot this RDM
plot_rdm(rdm, rescale=True)
#%%
"""********** Section 5: Calculating RDMs and Plotting **********"""
# Calculate the RDMs by a 10ms time-window
# (raw sampling requency is 1000Hz, so here time_win=10ms/(1s/1000Hz)/1000=10)
rdms = eegRDM(megdata, time_opt=1, time_win=10, time_step=10)
# Plot the RDM of 0ms, 50ms, 100ms, 150ms, 200ms
times = [10, 20, 30, 40, 50]
for t in times:
plot_rdm(rdms[t], rescale=True)
def eegANDfmri_corr(eeg_data, fmri_data, chl_opt=0, ksize=[3, 3, 3], strides=[1, 1, 1], sub_result=1, method="spearman", fisherz=False, rescale=False, permutation=False, iter=5000):
"""
Calculate the Similarities between EEG/MEG/fNIRS data and fMRI data for searchligt
Parameters
----------
eeg_data : array
The EEG/MEG/fNIRS data.
The shape of EEGdata must be [n_cons, n_subs, n_trials, n_chls, n_ts].
n_cons, n_subs, n_trials, n_chls & n_ts represent the number of conidtions, the number of subjects, the number
of trials, the number of channels & the number of time-points, respectively.
fmri_data : array
The fmri data.
The shape of fmri_data must be [n_cons, n_subs, nx, ny, nz]. n_cons, nx, ny, nz represent the number of
conditions, the number of subs & the size of fMRI-img, respectively.
chl_opt : int 0 or 1. Default is 0.
Calculate the RDM & similarities for each channel or not.
If chl_opt=0, calculating based on all channels' data.
If chl_opt=1, calculating based on each channel's data respectively.
ksize : array or list [kx, ky, kz]. Default is [3, 3, 3].
The size of the calculation unit for searchlight.
kx, ky, kz represent the number of voxels along the x, y, z axis.
strides : array or list [sx, sy, sz]. Default is [1, 1, 1].
The strides for calculating along the x, y, z axis.
sub_result: int 0 or 1. Default is 1.
Return the subject-result or average-result.
If sub_result=0, return the average result.
If sub_result=1, return the results of each subject.
method : string 'spearman' or 'pearson' or 'kendall' or 'similarity' or 'distance'. Default is 'spearman'.
The method to calculate the similarities.
If method='spearman', calculate the Spearman Correlations. If method='pearson', calculate the Pearson
Correlations. If methd='kendall', calculate the Kendall tau Correlations. If method='similarity', calculate the
Cosine Similarities. If method='distance', calculate the Euclidean Distances.
fisherz : bool True or False. Default is False.
Do the Fisher-Z transform of the RDMs or not.
rescale : bool True or False.
Rescale the values in RDM or not.
Here, the maximum-minimum method is used to rescale the values except for the values on the diagonal.
permutation : bool True or False. Default is False.
Use permutation test or not.
iter : int. Default is 5000.
The times for iteration.
Returns
-------
corrs : array
The similarities between EEG/MEG/fNIRS data and fMRI data for searchlight.
If chl_opt=1 & sub_result=1, the shape of corrs is [n_subs, n_chls, n_x, n_y, n_z, 2]. n_subs, n_x, n_y, n_z
represent the number of subjects, the number of calculation units for searchlight along the x, y, z axis and 2
represents a r-value and a p-value.
If chl_opt=1 & sub_result=0, the shape of corrs is [n_chls, n_x, n_y, n_z, 2]. n_x, n_y, n_z represent the
number of calculation units for searchlight along the x, y, z axis and 2 represents a r-value and a p-value.
If chl_opt=0 & sub_result=1, the shape of RDMs is [n_subs, n_x, n_y, n_z, 2]. n_subs, n_x, n_y, n_z represent
the number of subjects, the number of calculation units for searchlight along the x, y, z axis and 2 represents
a r-value and a p-value.
If chl_opt=0 & sub_result=0, the shape of corrs is [n_x, n_y, n_z, 2]. n_x, n_y, n_z represent the number of
calculation units for searchlight along the x, y, z axis and 2 represents a r-value and a p-value.
"""
if len(np.shape(eeg_data)) != 5 or len(np.shape(fmri_data)) != 5:
return "Invalid input!"
# get the number of subjects
subs = np.shape(fmri_data)[1]
# get the size of the fMRI-img
nx = np.shape(fmri_data)[2]
ny = np.shape(fmri_data)[3]
nz = np.shape(fmri_data)[4]
# the size of the calculation units for searchlight
kx = ksize[0]
ky = ksize[1]
kz = ksize[2]
# strides for calculating along the x, y, z axis
sx = strides[0]
sy = strides[1]
sz = strides[0]
# calculate the number of the calculation units in the x, y, z directions
n_x = int((nx - kx) / sx) + 1
n_y = int((ny - ky) / sy) + 1
n_z = int((nz - kz) / sz) + 1
# get the number of channels
chls = np.shape(eeg_data)[3]
# calculate the fmri_rdms for searchlight
fmri_rdms = fmriRDM(fmri_data, sub_result=sub_result, ksize=ksize, strides=strides)
print(fmri_rdms.shape)
# calculate the eeg_rdms
eeg_rdms = eegRDM(eeg_data, sub_opt=sub_result, chl_opt=chl_opt)
# chl_opt=1
if chl_opt == 1:
# sub_result=1
if sub_result == 1:
# chl_opt=1 & sub_result=1
# initialize the corrs
corrs = np.full([subs, chls, n_x, n_y, n_z, 2], np.nan)
# calculate the corrs
for sub in range(subs):
for j in range(n_x):
for k in range(n_y):
for l in range(n_z):
for i in range(chls):
if method == "spearman":
corrs[sub, i, j, k, l] = rdm_correlation_spearman(eeg_rdms[sub, i], fmri_rdms[sub, j, k, l], fisherz=fisherz,
rescale=rescale, permutation=permutation, iter=iter)
elif method == "pearson":
corrs[sub, i, j, k, l] = rdm_correlation_pearson(eeg_rdms[sub, i], fmri_rdms[sub, j, k, l], fisherz=fisherz,
rescale=rescale, permutation=permutation, iter=iter)
elif method == "kendall":
corrs[sub, i, j, k, l] = rdm_correlation_kendall(eeg_rdms[sub, i], fmri_rdms[sub, j, k, l], fisherz=fisherz,
rescale=rescale, permutation=permutation, iter=iter)
elif method == "similarity":
corrs[sub, i, j, k, l, 0] = rdm_similarity(eeg_rdms[sub, i], fmri_rdms[sub, j, k, l],
rescale=rescale)
elif method == "distance":
corrs[sub, i, j, k, l, 0] = rdm_distance(eeg_rdms[sub, i], fmri_rdms[sub, i, j, k],
rescale=rescale)
return np.abs(corrs)
# sub_result=0
# chl_opt=1 & sub_result=0
# initialize the corrs
corrs = np.full([chls, n_x, n_y, n_z, 2], np.nan)
# calculate the corrs
for j in range(n_x):
for k in range(n_y):
for l in range(n_z):
for i in range(chls):
if method == "spearman":
corrs[i, j, k, l] = rdm_correlation_spearman(eeg_rdms[i], fmri_rdms[j, k, l], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "pearson":
corrs[i, j, k, l] = rdm_correlation_pearson(eeg_rdms[i], fmri_rdms[j, k, l], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "kendall":
corrs[i, j, k, l] = rdm_correlation_kendall(eeg_rdms[i], fmri_rdms[j, k, l], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "similarity":
corrs[i, j, k, l, 0] = rdm_similarity(eeg_rdms[i], fmri_rdms[j, k, l], rescale=rescale)
elif method == "distance":
corrs[i, j, k, l, 0] = rdm_distance(eeg_rdms[i], fmri_rdms[i, j, k], rescale=rescale)
return np.abs(corrs)
# chl_opt=0
# sub_result=1
if sub_result == 1:
# chl_opt=0 & sub_result=1
# initialize the corrs
corrs = np.full([subs, n_x, n_y, n_z, 2], np.nan)
# calculate the corrs
for i in range(subs):
for j in range(n_x):
for k in range(n_y):
for l in range(n_z):
if method == "spearman":
corrs[i, j, k, l] = rdm_correlation_spearman(eeg_rdms[i], fmri_rdms[j, k, l], fisherz=fisherz,
rescale=rescale, permutation=permutation, iter=iter)
elif method == "pearson":
corrs[i, j, k, l] = rdm_correlation_pearson(eeg_rdms[i], fmri_rdms[j, k, l], fisherz=fisherz,
rescale=rescale, permutation=permutation, iter=iter)
elif method == "kendall":
corrs[i, j, k, l] = rdm_correlation_kendall(eeg_rdms[i], fmri_rdms[j, k, l], fisherz=fisherz,
rescale=rescale, permutation=permutation, iter=iter)
elif method == "similarity":
corrs[i, j, k, l, 0] = rdm_similarity(eeg_rdms[i], fmri_rdms[j, k, l], rescale=rescale)
elif method == "distance":
corrs[i, j, k, l, 0] = rdm_distance(eeg_rdms[i], fmri_rdms[i, j, k], rescale=rescale)
return corrs
# sub_result=0
# chl_opt=0 & sub_result=0
# initialize the corrs
corrs = np.full([n_x, n_y, n_z, 2], np.nan)
# calculate the corrs
for i in range(n_x):
for j in range(n_y):
for k in range(n_z):
if method == "spearman":
corrs[i, j, k] = rdm_correlation_spearman(eeg_rdms, fmri_rdms[i, j, k], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "pearson":
corrs[i, j, k] = rdm_correlation_pearson(eeg_rdms, fmri_rdms[i, j, k], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "kendall":
corrs[i, j, k] = rdm_correlation_kendall(eeg_rdms, fmri_rdms[i, j, k], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "similarity":
corrs[i, j, k, 0] = rdm_similarity(eeg_rdms, fmri_rdms[i, j, k], rescale=rescale)
elif method == "distance":
corrs[i, j, k, 0] = rdm_distance(eeg_rdms, fmri_rdms[i, j, k], rescale=rescale)
return np.abs(corrs)
def bhvANDeeg_corr(bhv_data, eeg_data, sub_opt=1, chl_opt=0, time_opt=0, time_win=5, time_step=5, method="spearman", fisherz=False, rescale=False, permutation=False, iter=5000):
"""
Calculate the Similarities between behavioral data and EEG/MEG/fNIRS data
Parameters
----------
bhv_data : array
The behavioral data.
The shape of bhv_data must be [n_cons, n_subs, n_trials].
n_cons, n_subs & n_trials represent the number of conidtions, the number of subjects & the number of trials,
respectively.
eeg_data : array
The EEG/MEG/fNIRS data.
The shape of EEGdata must be [n_cons, n_subs, n_trials, n_chls, n_ts].
n_cons, n_subs, n_trials, n_chls & n_ts represent the number of conidtions, the number of subjects, the number
of trials, the number of channels & the number of time-points, respectively.
sub_opt : int 0 or 1. Default is 0.
Calculate the RDM & similarities for each subject or not.
If sub_opt=0, calculating based on all data.
If sub_opt=1, calculating based on each subject's data, respectively.
chl_opt : int 0 or 1. Default is 0.
Calculate the RDM & similarities for each channel or not.
If chl_opt=0, calculating based on all channels' data.
If chl_opt=1, calculating based on each channel's data respectively.
time_opt : int 0 or 1. Default is 0.
Calculate the RDM & similarities for each time-point or not
If time_opt=0, calculating based on whole time-points' data.
If time_opt=1, calculating based on each time-points respectively.
time_win : int. Default is 5.
Set a time-window for calculating the RDM & similarities for different time-points.
Only when time_opt=1, time_win works.
If time_win=5, that means each calculation process based on 5 time-points.
time_step : int. Default is 5.
The time step size for each time of calculating.
Only when time_opt=1, time_step works.
method : string 'spearman' or 'pearson' or 'kendall' or 'similarity' or 'distance'. Default is 'spearman'.
The method to calculate the similarities.
If method='spearman', calculate the Spearman Correlations. If method='pearson', calculate the Pearson
Correlations. If methd='kendall', calculate the Kendall tau Correlations. If method='similarity', calculate the
Cosine Similarities. If method='distance', calculate the Euclidean Distances.
fisherz : bool True or False. Default is False.
Do the Fisher-Z transform of the RDMs or not.
rescale : bool True or False.
Rescale the values in RDM or not.
Here, the maximum-minimum method is used to rescale the values except for the
values on the diagonal.
permutation : bool True or False. Default is False.
Use permutation test or not.
iter : int. Default is 5000.
The times for iteration.
Returns
-------
corrs : array
The similarities between behavioral data and EEG/MEG/fNIRS data.
If sub_opt=0 & chl_opt=0 & time_opt=0, return one corr result.
The shape of corrs is [2], a r-value and a p-value. If method='similarity' or method='distance', the
p-value is 0.
If sub_opt=0 & chl_opt=0 & time_opt=1, return int((n_ts-time_win)/time_step)+1 corrs result.
The shape of corrs is [int((n_ts-time_win)/time_step)+1, 2]. 2 represents a r-value and a p-value. If
method='similarity' or method='distance', the p-values are all 0.
If sub_opt=0 & chl_opt=1 & time_opt=0, return n_chls corrs result.
The shape of corrs is [n_chls, 2]. 2 represents a r-value and a p-value. If method='similarity' or
method='distance', the p-values are all 0.
If sub_opt=0 & chl_opt=1 & time_opt=1, return n_chls*(int((n_ts-time_win)/time_step)+1) corrs result.
The shape of corrs is [n_chls, int((n_ts-time_win)/time_step)+1, 2]. 2 represents a r-value and a p-value.
If method='similarity' or method='distance', the p-values are all 0.
If sub_opt=1 & chl_opt=0 & time_opt=0, return n_subs corr result.
The shape of corrs is [n_subs, 2], a r-value and a p-value. If method='similarity' or method='distance',
the p-values are all 0.
If sub_opt=1 & chl_opt=0 & time_opt=1, return n_subs*(int((n_ts-time_win)/time_step)+1) corrs result.
The shape of corrs is [n_subs, int((n_ts-time_win)/time_step)+1, 2]. 2 represents a r-value and a p-value.
If method='similarity' or method='distance', the p-values are all 0.
If sub_opt=1 & chl_opt=1 & time_opt=0, return n_subs*n_chls corrs result.
The shape of corrs is [n_subs, n_chls, 2]. 2 represents a r-value and a p-value. If method='similarity' or
method='distance', the p-values are all 0.
If sub_opt=1 & chl_opt=1 & time_opt=1, return n_subs*n_chls*(int((n_ts-time_win)/time_step)+1) corrs result.
The shape of corrs is [n_subs, n_chls, int((n_ts-time_win)/time_step)+1, 2]. 2 represents a r-value and a
p-value. If method='similarity' or method='distance', the p-values are all 0.
"""
if len(np.shape(bhv_data)) != 3 or len(np.shape(eeg_data)) != 5:
return "Invalid input!"
# get the number of subjects
subs = np.shape(bhv_data)[1]
# get the number of channels
chls = np.shape(eeg_data)[3]
# get the number of time-points for calculating
ts = np.shape(eeg_data)[4]
ts = int((ts-time_win)/time_step)+1
if sub_opt == 1:
# calculate the bhv_rdm
bhv_rdms = bhvRDM(bhv_data, sub_opt=sub_opt)
if chl_opt == 0:
if time_opt == 0:
# sub_opt=1 & chl_opt=0 & time_opt=0
# calculate the eeg_rdms
eeg_rdms = eegRDM(eeg_data, sub_opt=sub_opt, chl_opt=chl_opt, time_opt=time_opt)
# initialize the corrs
corrs = np.zeros([subs, 2], dtype=np.float64)
# calculate the corrs
for i in range(subs):
if method == "spearman":
corrs[i] = rdm_correlation_spearman(bhv_rdms[i], eeg_rdms[i], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "pearson":
corrs[i] = rdm_correlation_pearson(bhv_rdms[i], eeg_rdms[i], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "kendall":
corrs[i] = rdm_correlation_kendall(bhv_rdms[i], eeg_rdms[i], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "similarity":
corrs[i, 0] = rdm_similarity(bhv_rdms[i], eeg_rdms[i], rescale=rescale)
elif method == "distance":
corrs[i, 0] = rdm_distance(bhv_rdms[i], eeg_rdms[i], rescale=rescale)
return corrs
# sub_opt=1 & chl_opt=0 & time_opt=1
# calculate the eeg_rdms
eeg_rdms = eegRDM(eeg_data, sub_opt=sub_opt, chl_opt=chl_opt, time_opt=time_opt, time_win=time_win, time_step=time_step)
# initialize the corrs
corrs = np.zeros([subs, ts, 2], dtype=np.float64)
# calculate the corrs
for i in range(subs):
for j in range(ts):
if method == "spearman":
corrs[i, j] = rdm_correlation_spearman(bhv_rdms[i], eeg_rdms[i, j], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "pearson":
corrs[i, j] = rdm_correlation_pearson(bhv_rdms[i], eeg_rdms[i, j], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "kendall":
corrs[i, j] = rdm_correlation_kendall(bhv_rdms[i], eeg_rdms[i, j], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "similarity":
corrs[i, j, 0] = rdm_similarity(bhv_rdms[i], eeg_rdms[i, j], rescale=rescale)
elif method == "distance":
corrs[i, j, 0] = rdm_distance(bhv_rdms[i], eeg_rdms[i, j], rescale=rescale)
return corrs
if time_opt == 1:
# sub_opt=1 & chl_opt=1 & time_opt=1
# calculate the eeg_rdms
eeg_rdms = eegRDM(eeg_data, sub_opt=sub_opt, chl_opt=chl_opt, time_opt=time_opt, time_win=time_win, time_step=time_step)
# initialize the corrs
corrs = np.zeros([subs, chls, ts, 2], dtype=np.float64)
# calculate the corrs
for i in range(subs):
for j in range(chls):
for k in range(ts):
if method == "spearman":
corrs[i, j, k] = rdm_correlation_spearman(bhv_rdms[i], eeg_rdms[i, j, k], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "pearson":
corrs[i, j, k] = rdm_correlation_pearson(bhv_rdms[i], eeg_rdms[i, j, k], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "kendall":
corrs[i, j, k] = rdm_correlation_kendall(bhv_rdms[i], eeg_rdms[i, j, k], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "similarity":
corrs[i, j, k, 0] = rdm_similarity(bhv_rdms[i], eeg_rdms[i, j, k], rescale=rescale)
elif method == "distance":
corrs[i, j, k, 0] = rdm_distance(bhv_rdms[i], eeg_rdms[i, j, k], rescale=rescale)
return corrs
# sub_opt=1 & chl_opt=1 & time_opt=0
eeg_rdms = eegRDM(eeg_data, sub_opt=sub_opt, chl_opt=chl_opt, time_opt=time_opt)
corrs = np.zeros([subs, chls, 2], dtype=np.float64)
for i in range(subs):
for j in range(chls):
if method == "spearman":
corrs[i, j] = rdm_correlation_spearman(bhv_rdms[i], eeg_rdms[i, j], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "pearson":
corrs[i, j] = rdm_correlation_pearson(bhv_rdms[i], eeg_rdms[i, j], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "kendall":
corrs[i, j] = rdm_correlation_kendall(bhv_rdms[i], eeg_rdms[i, j], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "similarity":
corrs[i, j, 0] = rdm_similarity(bhv_rdms[i], eeg_rdms[i, j], rescale=rescale)
elif method == "distance":
corrs[i, j, 0] = rdm_distance(bhv_rdms[i], eeg_rdms[i, j], rescale=rescale)
return corrs
# if sub_opt=0
bhv_rdm = bhvRDM(bhv_data, sub_opt=sub_opt)
if chl_opt == 1:
if time_opt == 1:
# sub_opt = 0 & chl_opt = 1 & time_opt = 1
# calculate the eeg_rdms
eeg_rdms = eegRDM(eeg_data, sub_opt=sub_opt, chl_opt=chl_opt, time_opt=time_opt, time_win=time_win, time_step=time_step)
# initialize the corrs
corrs = np.zeros([chls, ts, 2], dtype=np.float64)
# calculate the corrs
for i in range(chls):
for j in range(ts):
if method == "spearman":
corrs[i, j] = rdm_correlation_spearman(bhv_rdm, eeg_rdms[i, j], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "pearson":
corrs[i, j] = rdm_correlation_pearson(bhv_rdm, eeg_rdms[i, j], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "kendall":
corrs[i, j] = rdm_correlation_kendall(bhv_rdm, eeg_rdms[i, j], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "similarity":
corrs[i, j, 0] = rdm_similarity(bhv_rdm, eeg_rdms[i, j], rescale=rescale)
elif method == "distance":
corrs[i, j, 0] = rdm_distance(bhv_rdm, eeg_rdms[i, j], rescale=rescale)
return corrs
# sub_opt=0 & chl_opt=1 & time_opt=0
# calculate the eeg_rdms
eeg_rdms = eegRDM(eeg_data, sub_opt=sub_opt, chl_opt=chl_opt, time_opt=time_opt)
# initialize the corrs
corrs = np.zeros([chls, 2], dtype=np.float64)
# calculate the corrs
for i in range(chls):
if method == "spearman":
corrs[i] = rdm_correlation_spearman(bhv_rdm, eeg_rdms[i], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "pearson":
corrs[i] = rdm_correlation_pearson(bhv_rdm, eeg_rdms[i], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "kendall":
corrs[i] = rdm_correlation_kendall(bhv_rdm, eeg_rdms[i], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "similarity":
corrs[i, 0] = rdm_similarity(bhv_rdm, eeg_rdms[i], rescale=rescale)
elif method == "distance":
corrs[i, 0] = rdm_distance(bhv_rdm, eeg_rdms[i], rescale=rescale)
return corrs
# if chl_opt=0
if time_opt == 1:
# sub_opt=0 & chl_opt=0 & time_opt=1
# calculate the eeg_rdms
eeg_rdms = eegRDM(eeg_data, sub_opt=sub_opt, chl_opt=chl_opt, time_opt=time_opt, time_win=time_win, time_step=time_step)
# initialize the corrs
corrs = np.zeros([ts, 2], dtype=np.float64)
# calculate the corrs
for i in range(ts):
if method == "spearman":
corrs[i] = rdm_correlation_spearman(bhv_rdm, eeg_rdms[i], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "pearson":
corrs[i] = rdm_correlation_pearson(bhv_rdm, eeg_rdms[i], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "kendall":
corrs[i] = rdm_correlation_kendall(bhv_rdm, eeg_rdms[i], fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "similarity":
corrs[i, 0] = rdm_similarity(bhv_rdm, eeg_rdms[i], rescale=rescale)
elif method == "distance":
corrs[i, 0] = rdm_distance(bhv_rdm, eeg_rdms[i], rescale=rescale)
return corrs
# sub_opt=0 & chl_opt=0 & time_opt=0
# calculate the eeg_rdms
eeg_rdm = eegRDM(eeg_data, sub_opt=sub_opt, chl_opt=chl_opt, time_opt=time_opt)
# initialize the corrs
corr = np.zeros([2], dtype=np.float64)
# calculate the corrs
if method == "spearson":
corr = rdm_correlation_spearman(bhv_rdm, eeg_rdm, fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "pearson":
corr = rdm_correlation_pearson(bhv_rdm, eeg_rdm, fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "kendall":
corr = rdm_correlation_kendall(bhv_rdm, eeg_rdm, fisherz=fisherz, rescale=rescale, permutation=permutation, iter=iter)
elif method == "similarity":
corr[0] = rdm_similarity(bhv_rdm, eeg_rdm, rescale=rescale)
elif method == "distance":
corr[0] = rdm_distance(bhv_rdm, eeg_rdm, rescale=rescale)
return corr
for sub in subs:
print('Loading data of sub' + sub)
f = h5py.File(data_dir + 'data_for_RSA/ERP/' + sub + '.h5', 'r')
ori_subdata = np.array(f['ori'])
pos_subdata = np.array(f['pos'])
f.close()
data_ori_ERP[:, subindex] = ori_subdata
data_pos_ERP[:, subindex] = pos_subdata
subindex = subindex + 1
# calculate the RDMs
print("\nCalculate the Orientation RDMs!")
RDM_ori_ERP = eegRDM(data_ori_ERP,
sub_opt=1,
chl_opt=0,
time_opt=1,
time_win=5,
time_step=5)
print("\nCalculate the Position RDMs!")
RDM_pos_ERP = eegRDM(data_pos_ERP,
sub_opt=1,
chl_opt=0,
time_opt=1,
time_win=5,
time_step=5)
# shape of RDMs: [5, 100, 16, 16]
# establish a Coding RDM
model_RDM = np.zeros([16, 16], dtype=np.float)
for i in range(16):
for j in range(16):
# -*- coding: utf-8 """ @File : rdm_cal.py @Author : Zitong Lu @Contact : [email protected] @License : MIT License """ import numpy as np from neurora.rdm_cal import eegRDM for sub in range(6): data = np.loadtxt("../eegdata/alpha_power_po/sub" + str(sub + 1) + ".txt") data = np.reshape(data, [18, 1, 1, 17, 875]) eegrdms = eegRDM(data, time_opt=1, time_win=25, time_step=1) eegrdms = np.reshape(eegrdms, [851, 18 * 18]) np.savetxt("../eegrdm/alpha_power_po/sub" + str(sub + 1) + ".txt", eegrdms)
def eegANDfmri_corr(eeg_data,
fmri_data,
chl_opt=0,
ksize=[3, 3, 3],
strides=[1, 1, 1],
method="spearman",
rescale=False):
"""
Calculate the Similarities between EEG/MEG/fNIRS data and fMRI data for searchligt
Parameters
----------
eeg_data : array
The EEG/MEG/fNIRS data.
The shape of EEGdata must be [n_cons, n_subs, n_trials, n_chls, n_ts].
n_cons, n_subs, n_trials, n_chls & n_ts represent the number of conidtions, the number of subjects, the number
of trials, the number of channels & the number of time-points, respectively.
fmri_data : array
The fmri data.
The shape of fmri_data must be [n_cons, n_subs, n_chls, nx, ny, nz].
n_cons, n_chls, nx, ny, nz represent the number of conidtions, the number of channels &
the size of fMRI-img, respectively.
chl_opt : int 0 or 1. Default is 0.
Calculate the RDM & similarities for each channel or not.
If chl_opt=0, calculating based on all channels' data.
If chl_opt=1, calculating based on each channel's data respectively.
ksize : array or list [kx, ky, kz]. Default is [3, 3, 3].
The size of the fMRI-img. nx, ny, nz represent the number of voxels along the x, y, z axis.
strides : array or list [sx, sy, sz]. Default is [1, 1, 1].
The strides for calculating along the x, y, z axis.
method : string 'spearman' or 'pearson' or 'kendall' or 'similarity' or 'distance'. Default is 'spearman'.
The method to calculate the similarities.
If method='spearman', calculate the Spearman Correlations. If method='pearson', calculate the Pearson
Correlations. If methd='kendall', calculate the Kendall tau Correlations. If method='similarity', calculate the
Cosine Similarities. If method='distance', calculate the Euclidean Distances.
rescale : bool True or False.
Rescale the values in RDM or not.
Here, the maximum-minimum method is used to rescale the values except for the values on the diagonal.
Returns
-------
corrs : array
The similarities between EEG/MEG/fNIRS data and fMRI data for searchlight.
If chl_opt=1, the shape of RDMs is [n_chls, n_x, n_y, n_z, 2]. n_x, n_y, n_z represent the number of calculation
units for searchlight along the x, y, z axis and 2 represents a r-value and a p-value.
If chl_opt=0, the shape of RDMs is [n_x, n_y, n_z, 2]. n_x, n_y, n_z represent the number of calculation units
for searchlight along the x, y, z axis and 2 represents a r-value and a p-value.
"""
# get the size of the fMRI-img
nx = np.shape(fmri_data)[2]
ny = np.shape(fmri_data)[3]
nz = np.shape(fmri_data)[4]
# the size of the calculation units for searchlight
kx = ksize[0]
ky = ksize[1]
kz = ksize[2]
# strides for calculating along the x, y, z axis
sx = strides[0]
sy = strides[1]
sz = strides[0]
# calculate the number of the calculation units in the x, y, z directions
n_x = int((nx - kx) / sx) + 1
n_y = int((ny - ky) / sy) + 1
n_z = int((nz - kz) / sz) + 1
# calculate the fmri_rdms for searchlight
fmri_rdms = fmriRDM(fmri_data, ksize=ksize, strides=strides)
# chl_opt=1
if chl_opt == 1:
# get the number of channels
chls = np.shape(eeg_data)[3]
# get the eeg_rdms
eeg_rdms = eegRDM(eeg_data, sub_opt=0, chl_opt=1, time_opt=0)
# initialize the corrs
corrs = np.full([chls, n_x, n_y, n_z, 2], np.nan)
# calculate the corrs
for j in range(n_x):
for k in range(n_y):
for l in range(n_z):
for i in range(chls):
if method == "spearman":
corrs[i, j, k, l] = rdm_correlation_spearman(
eeg_rdms[i],
fmri_rdms[j, k, l],
rescale=rescale)
elif method == "pearson":
corrs[i, j, k,
l] = rdm_correlation_pearson(eeg_rdms[i],
fmri_rdms[j, k,
l],
rescale=rescale)
elif method == "kendall":
corrs[i, j, k,
l] = rdm_correlation_kendall(eeg_rdms[i],
fmri_rdms[j, k,
l],
rescale=rescale)
elif method == "similarity":
corrs[i, j, k, l,
0] = rdm_similarity(eeg_rdms[i],
fmri_rdms[j, k, l],
rescale=rescale)
elif method == "distance":
corrs[i, j, k, l,
0] = rdm_distance(eeg_rdms[i],
fmri_rdms[i, j, k],
rescale=rescale)
return np.abs(corrs)
# chl_opt=0
# get the eeg_rdms
eeg_rdm = eegRDM(eeg_data, sub_opt=0, chl_opt=0, time_opt=0)
# initialize the corrs
corrs = np.full([n_x, n_y, n_z, 2], np.nan)
# calculate the corrs
for i in range(n_x):
for j in range(n_y):
for k in range(n_z):
if method == "spearman":
corrs[i, j, k] = rdm_correlation_spearman(eeg_rdm,
fmri_rdms[i, j,
k],
rescale=rescale)
elif method == "pearson":
corrs[i, j, k] = rdm_correlation_pearson(eeg_rdm,
fmri_rdms[i, j,
k],
rescale=rescale)
elif method == "kendall":
corrs[i, j, k] = rdm_correlation_kendall(eeg_rdm,
fmri_rdms[i, j,
k],
rescale=rescale)
elif method == "similarity":
corrs[i, j, k, 0] = rdm_similarity(eeg_rdm,
fmri_rdms[i, j, k],
rescale=rescale)
elif method == "distance":
corrs[i, j, k, 0] = rdm_distance(eeg_rdm,
fmri_rdms[i, j, k],
rescale=rescale)
return np.abs(corrs)
def eegANDfmri_corr(eeg_data,
fmri_data,
chl_opt=0,
ksize=[3, 3, 3],
strides=[1, 1, 1],
method="spearman",
rescale=False):
# sub_opt=0, time_opt=0
nx = np.shape(fmri_data)[2]
ny = np.shape(fmri_data)[3]
nz = np.shape(fmri_data)[4]
cons = np.shape(eeg_data)[0]
kx = ksize[0]
ky = ksize[1]
kz = ksize[2]
sx = strides[0]
sy = strides[1]
sz = strides[0]
n_x = int((nx - kx) / sx) + 1
n_y = int((ny - ky) / sy) + 1
n_z = int((nz - kz) / sz) + 1
fmri_rdms = fmriRDM(fmri_data, ksize=ksize, strides=strides)
if chl_opt == 1:
chls = np.shape(eeg_data)[3]
eeg_rdms = eegRDM(eeg_data, sub_opt=0, chl_opt=1, time_opt=0)
corrs = np.full([chls, n_x, n_y, n_z, 2], np.nan)
for j in range(n_x):
for k in range(n_y):
for l in range(n_z):
"""index = 0
for m in range(cons):
for n in range(cons):
if math.isnan(fmri_rdms[j, k, l]) == True:
index = index + 1"""
for i in range(chls):
"""if index != 0:
corrs[i, j, k, l, 0] = 0
corrs[i, j, k, l, 1] = 1"""
if method == "spearman":
corrs[i, j, k, l] = rdm_correlation_spearman(
eeg_rdms[i],
fmri_rdms[j, k, l],
rescale=rescale)
elif method == "pearson":
corrs[i, j, k,
l] = rdm_correlation_pearson(eeg_rdms[i],
fmri_rdms[j, k,
l],
rescale=rescale)
elif method == "kendall":
corrs[i, j, k,
l] = rdm_correlation_kendall(eeg_rdms[i],
fmri_rdms[j, k,
l],
rescale=rescale)
elif method == "similarity":
corrs[i, j, k, l,
0] = rdm_similarity(eeg_rdms[i],
fmri_rdms[j, k, l],
rescale=rescale)
elif method == "distance":
corrs[i, j, k, l,
0] = rdm_distance(eeg_rdms[i],
fmri_rdms[i, j, k],
rescale=rescale)
return np.abs(corrs)
# if chl_opt=0
eeg_rdm = eegRDM(eeg_data, sub_opt=0, chl_opt=0, time_opt=0)
corrs = np.full([n_x, n_y, n_z, 2], np.nan)
for i in range(n_x):
for j in range(n_y):
for k in range(n_z):
"""index = 0
for m in range(cons):
for n in range(cons):
if math.isnan(fmri_rdms[i, j, k]) == True:
index = index + 1
if index != 0:
corrs[i, j, k, 0] = 0
corrs[i, j, k, 1] = 1"""
if method == "spearman":
corrs[i, j, k] = rdm_correlation_spearman(eeg_rdm,
fmri_rdms[i, j,
k],
rescale=rescale)
elif method == "pearson":
corrs[i, j, k] = rdm_correlation_pearson(eeg_rdm,
fmri_rdms[i, j,
k],
rescale=rescale)
elif method == "kendall":
corrs[i, j, k] = rdm_correlation_kendall(eeg_rdm,
fmri_rdms[i, j,
k],
rescale=rescale)
elif method == "similarity":
corrs[i, j, k, 0] = rdm_similarity(eeg_rdm,
fmri_rdms[i, j, k],
rescale=rescale)
elif method == "distance":
corrs[i, j, k, 0] = rdm_distance(eeg_rdm,
fmri_rdms[i, j, k],
rescale=rescale)
return np.abs(corrs)
def bhvANDeeg_corr(bhv_data,
EEG_data,
sub_opt=0,
bhv_data_opt=1,
time_win=5,
chl_opt=0,
time_opt=0,
method="spearman",
rescale=False):
subs = np.shape(bhv_data)[1]
chls = np.shape(EEG_data)[3]
ts = int(np.shape(EEG_data)[4] / time_win)
if sub_opt == 1:
if bhv_data_opt == 0:
return None
# if bhv_data_opt=1
bhv_rdms = bhvRDM(bhv_data, sub_opt=sub_opt, data_opt=bhv_data_opt)
if chl_opt == 0:
if time_opt == 0:
eeg_rdms = eegRDM(EEG_data,
time_win=time_win,
sub_opt=sub_opt,
chl_opt=chl_opt,
time_opt=time_opt)
corrs = np.zeros([subs, 2], dtype=np.float64)
for i in range(subs):
if method == "spearman":
corrs[i] = rdm_correlation_spearman(bhv_rdms[i],
eeg_rdms[i],
rescale=rescale)
elif method == "pearson":
corrs[i] = rdm_correlation_pearson(bhv_rdms[i],
eeg_rdms[i],
rescale=rescale)
elif method == "kendall":
corrs[i] = rdm_correlation_kendall(bhv_rdms[i],
eeg_rdms[i],
rescale=rescale)
elif method == "similarity":
corrs[i, 0] = rdm_similarity(bhv_rdms[i],
eeg_rdms[i],
rescale=rescale)
elif method == "distance":
corrs[i, 0] = rdm_distance(bhv_rdms[i],
eeg_rdms[i],
rescale=rescale)
return corrs
# if time_opt=1
eeg_rdms = eegRDM(EEG_data,
sub_opt=sub_opt,
chl_opt=chl_opt,
time_opt=time_opt)
corrs = np.zeros([subs, ts, 2], dtype=np.float64)
for i in range(subs):
for j in range(ts):
if method == "spearman":
corrs[i, j] = rdm_correlation_spearman(bhv_rdms[i],
eeg_rdms[i, j],
rescale=rescale)
elif method == "pearson":
corrs[i, j] = rdm_correlation_pearson(bhv_rdms[i],
eeg_rdms[i, j],
rescale=rescale)
elif method == "kendall":
corrs[i, j] = rdm_correlation_kendall(bhv_rdms[i],
eeg_rdms[i, j],
rescale=rescale)
elif method == "similarity":
corrs[i, j, 0] = rdm_similarity(bhv_rdms[i],
eeg_rdms[i, j],
rescale=rescale)
elif method == "distance":
corrs[i, j, 0] = rdm_distance(bhv_rdms[i],
eeg_rdms[i, j],
rescale=rescale)
return corrs
# chl_opt=1
if time_opt == 1:
return None
# time_opt=0
eeg_rdms = eegRDM(EEG_data,
sub_opt=sub_opt,
chl_opt=chl_opt,
time_opt=time_opt)
corrs = np.zeros([subs, chls], dtype=np.float64)
for i in range(subs):
for j in range(chls):
if method == "spearman":
corrs[i, j] = rdm_correlation_spearman(bhv_rdms[i],
eeg_rdms[i, j],
rescale=rescale)
elif method == "pearson":
corrs[i, j] = rdm_correlation_pearson(bhv_rdms[i],
eeg_rdms[i, j],
rescale=rescale)
elif method == "kendall":
corrs[i, j] = rdm_correlation_kendall(bhv_rdms[i],
eeg_rdms[i, j],
rescale=rescale)
elif method == "similarity":
corrs[i, j, 0] = rdm_similarity(bhv_rdms[i],
eeg_rdms[i, j],
rescale=rescale)
elif method == "distance":
corrs[i, j, 0] = rdm_distance(bhv_rdms[i],
eeg_rdms[i, j],
rescale=rescale)
return corrs
# if sub_opt=0
bhv_rdm = bhvRDM(bhv_data, sub_opt=sub_opt, data_opt=bhv_data_opt)
if chl_opt == 1:
if time_opt == 1:
eeg_rdms = eegRDM(EEG_data,
sub_opt=sub_opt,
chl_opt=chl_opt,
time_opt=time_opt)
corrs = np.zeros([chls, ts, 2], dtype=np.float64)
for i in range(chls):
for j in range(ts):
if method == "spearman":
corrs[i, j] = rdm_correlation_spearman(bhv_rdm,
eeg_rdms[i, j],
rescale=rescale)
elif method == "pearson":
corrs[i, j] = rdm_correlation_pearson(bhv_rdm,
eeg_rdms[i, j],
rescale=rescale)
elif method == "kendall":
corrs[i, j] = rdm_correlation_kendall(bhv_rdm,
eeg_rdms[i, j],
rescale=rescale)
elif method == "similarity":
corrs[i, j, 0] = rdm_similarity(bhv_rdm,
eeg_rdms[i, j],
rescale=rescale)
elif method == "distance":
corrs[i, j, 0] = rdm_distance(bhv_rdm,
eeg_rdms[i, j],
rescale=rescale)
return corrs
# if time_opt=0
eeg_rdms = eegRDM(EEG_data,
sub_opt=sub_opt,
chl_opt=chl_opt,
time_opt=time_opt)
corrs = np.zeros([chls, 2], dtype=np.float64)
for i in range(chls):
if method == "spearman":
corrs[i] = rdm_correlation_spearman(bhv_rdm,
eeg_rdms[i],
rescale=rescale)
elif method == "pearson":
corrs[i] = rdm_correlation_pearson(bhv_rdm,
eeg_rdms[i],
rescale=rescale)
elif method == "kendall":
corrs[i] = rdm_correlation_kendall(bhv_rdm,
eeg_rdms[i],
rescale=rescale)
elif method == "similarity":
corrs[i, 0] = rdm_similarity(bhv_rdm, eeg_rdms[i])
elif method == "distance":
corrs[i, 0] = rdm_distance(bhv_rdm, eeg_rdms[i])
return corrs
# if chl_opt=0
if time_opt == 1:
eeg_rdms = eegRDM(EEG_data, sub_opt=0, chl_opt=0, time_opt=1)
corrs = np.zeros([ts, 2], dtype=np.float64)
for i in range(ts):
if method == "spearman":
corrs[i] = rdm_correlation_spearman(bhv_rdm,
eeg_rdms[i],
rescale=rescale)
elif method == "pearson":
corrs[i] = rdm_correlation_pearson(bhv_rdm,
eeg_rdms[i],
rescale=rescale)
elif method == "kendall":
corrs[i] = rdm_correlation_kendall(bhv_rdm,
eeg_rdms[i],
rescale=rescale)
elif method == "similarity":
corrs[i, 0] = rdm_similarity(bhv_rdm,
eeg_rdms[i],
rescale=rescale)
elif method == "distance":
corrs[i, 0] = rdm_distance(bhv_rdm,
eeg_rdms[i],
rescale=rescale)
return corrs
# if time_opt=0
eeg_rdm = eegRDM(EEG_data, sub_opt=0, chl_opt=0, time_opt=0)
corr = np.zeros([2], dtype=np.float64)
if method == "spearson":
corr = rdm_correlation_spearman(bhv_rdm, eeg_rdm, rescale=rescale)
elif method == "pearson":
corr = rdm_correlation_pearson(bhv_rdm, eeg_rdm, rescale=rescale)
elif method == "kendall":
corr = rdm_correlation_kendall(bhv_rdm, eeg_rdm, rescale=rescale)
elif method == "similarity":
corr[0] = rdm_similarity(bhv_rdm, eeg_rdm, rescale=rescale)
elif method == "distance":
corr[0] = rdm_distance(bhv_rdm, eeg_rdm, rescale=rescale)
return corr
megdata[subindex] = subdata
subindex = subindex + 1
# the shape of MEG data: megdata is [3, 92, 306, 1101]
# n_subs = 3, n_conditions = 92, n_channels = 306, n_timepoints = 1101 (-100ms to 1000ms)
"""********** Section 2: Calculating single RDM and Plotting **********"""
# shape of megdata: [n_subs, n_cons, n_chls, n_ts] -> [n_cons, n_subs, n_chls, n_ts]
megdata = np.transpose(megdata, (1, 0, 2, 3))
# shape of megdata: [n_cons, n_subs, n_chls, n_ts] -> [n_cons, n_subs, n_trials, n_chls, n_ts]
# here data is averaged, so set n_trials = 1
megdata = np.reshape(megdata, [92, 3, 1, 306, 1101])
# Calculate the RDM based on the data during 190ms-210ms
rdm = eegRDM(megdata[:, :, :, :, 290:310])
# Plot this RDM
plot_rdm(rdm, rescale=True)
"""********** Section 3: Calculating RDMs and Plotting **********"""
# Calculate the RDMs by a 10ms time-window
# (raw sampling requency is 1000Hz, so here time_win=10ms/(1s/1000Hz)/1000=10)
rdms = eegRDM(megdata, time_win=10, time_opt=1)
# Plot the RDM of 0ms, 50ms, 100ms, 150ms, 200ms
times = [0, 10, 20, 30, 40, 50]
for t in times:
plot_rdm(rdms[t], rescale=True)
"""********** Section 4: Calculating the Similarity between two RDMs **********"""
